'''
    This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

    PM4Py is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    PM4Py is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with PM4Py.  If not, see <https://www.gnu.org/licenses/>.
'''

from pm4py.statistics.attributes.log import get as attr_get
from pm4py.objects.dfg.utils import dfg_utils
from pm4py.util import xes_constants as xes
from pm4py.util import exec_utils
from pm4py.statistics.service_time.log import get as serv_time_get
from enum import Enum
from pm4py.util import constants
from enum import Enum
from collections import Counter

from typing import Optional, Dict, Any, Tuple
import graphviz
from pm4py.objects.log.obj import EventLog
from collections import Counter
from pm4py.visualization.dfg.util import dfg_gviz


class Parameters(Enum):
    ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
    FORMAT = "format"
    MAX_NO_EDGES_IN_DIAGRAM = "maxNoOfEdgesInDiagram"
    START_ACTIVITIES = "start_activities"
    END_ACTIVITIES = "end_activities"
    TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
    START_TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY
    FONT_SIZE = "font_size"
    AGGREGATION_MEASURE = "aggregation_measure"
    RANKDIR = "rankdir"
    BGCOLOR = "bgcolor"
    STAT_LOCALE = "stat_locale"

def apply(dfg: Dict[Tuple[str, str], int], log: EventLog = None, parameters: Optional[Dict[Any, Any]] = None, activities_count : Dict[str, int] = None, serv_time: Dict[str, float] = None) -> graphviz.Digraph:
    """
    Visualize a performance directly-follows graph

    Parameters
    -----------------
    dfg
        Performance Directly-follows graph
    log
        (if provided) Event log for the calculation of statistics
    activities_count
        (if provided) Dictionary associating to each activity the number of occurrences in the log.
    serv_time
        (if provided) Dictionary associating to each activity the average service time
    parameters
        Variant-specific parameters

    Returns
    -----------------
    gviz
        Graphviz digraph
    """
    if parameters is None:
        parameters = {}

    activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes.DEFAULT_NAME_KEY)
    image_format = exec_utils.get_param_value(Parameters.FORMAT, parameters, "png")
    max_no_of_edges_in_diagram = exec_utils.get_param_value(Parameters.MAX_NO_EDGES_IN_DIAGRAM, parameters, 100000)
    start_activities = exec_utils.get_param_value(Parameters.START_ACTIVITIES, parameters, [])
    end_activities = exec_utils.get_param_value(Parameters.END_ACTIVITIES, parameters, [])
    font_size = exec_utils.get_param_value(Parameters.FONT_SIZE, parameters, 12)
    font_size = str(font_size)
    activities = dfg_utils.get_activities_from_dfg(dfg)
    aggregation_measure = exec_utils.get_param_value(Parameters.AGGREGATION_MEASURE, parameters, "mean")

    rankdir = exec_utils.get_param_value(Parameters.RANKDIR, parameters, constants.DEFAULT_RANKDIR_GVIZ)
    bgcolor = exec_utils.get_param_value(Parameters.BGCOLOR, parameters, constants.DEFAULT_BGCOLOR)
    stat_locale = exec_utils.get_param_value(Parameters.STAT_LOCALE, parameters, {})

    # if all the aggregation measures are provided for a given key,
    # then pick one of the values for the representation
    dfg0 = dfg
    dfg = {}
    for key in dfg0:
        try:
            if aggregation_measure in dfg0[key]:
                dfg[key] = dfg0[key][aggregation_measure]
            else:
                dfg[key] = dfg0[key]
        except:
            dfg[key] = dfg0[key]
    
    if activities_count is None:
        if log is not None:
            activities_count = attr_get.get_attribute_values(log, activity_key, parameters=parameters)
        else:
            # the frequency of an activity in the log is at least the number of occurrences of
            # incoming arcs in the DFG.
            # if the frequency of the start activities nodes is also provided, use also that.
            activities_count = Counter({key: 0 for key in activities})
            for el in dfg:
                activities_count[el[1]] += dfg[el]
            if isinstance(start_activities, dict):
                for act in start_activities:
                    activities_count[act] += start_activities[act]

    if serv_time is None:
        if log is not None:
            serv_time = serv_time_get.apply(log, parameters=parameters)
        else:
            serv_time = {key: -1 for key in activities}

    return dfg_gviz.graphviz_visualization(activities_count, dfg, image_format=image_format, measure="performance",
                                           max_no_of_edges_in_diagram=max_no_of_edges_in_diagram,
                                           start_activities=start_activities, end_activities=end_activities, serv_time=serv_time,
                                           font_size=font_size, bgcolor=bgcolor, rankdir=rankdir)
